Code for InfoCTM: A Mutual Information Maximization Perspective of Cross-lingual Topic Modeling (AAAI2023)

Check our latest topic modeling toolkit TopMost !

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Usage

1. Prepare Environment

python=3.7
torch==1.7.1
scikit-learn==1.0.2
gensim==4.0.1
pyyaml==6.0
spacy==2.3.2

2. Training

We provide a shell script for training:

./run.sh

3. Evaluation

Topic coherence:

We have released the implementation of CNPMI.

Topic diversity:

We use the average $TU$ score of two langauges:

python utils/TU.py --path {path of topic words in language 1}
python utils/TU.py --path {path of topic words in language 2}

Citation

If you want to use our code, please cite as

@article{wu2023infoctm,
title={InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling},
author={Wu, Xiaobao and Dong, Xinshuai and Nguyen, Thong and Liu, Chaoqun and Pan, Liangming and Luu, Anh Tuan},
journal={arXiv preprint arXiv:2304.03544},
year={2023}
}